This is text. I am going to load a data set and clean it. The data set is summarized below:
library(tidyverse)
path <- "../Data/Soil_Predators.csv"
df <- read_csv(path,skip=1,col_names=FALSE)
badcolnames <- readLines(path,n=1)
badcolnames <- badcolnames %>% str_replace_all(",_","_") %>% str_split(",") %>% unlist()
df <- df %>% select(-c(X25,X26))
names(df) <- badcolnames
skimr::skim(df) %>%
as.data.frame() %>%
kableExtra::kable() %>%
kableExtra::kable_classic(lightable_options="hover")| skim_type | skim_variable | n_missing | complete_rate | character.min | character.max | character.empty | character.n_unique | character.whitespace | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| character | Predator_order | 0 | 1.0000000 | 14 | 14 | 0 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | Predator_family | 0 | 1.0000000 | 11 | 13 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | Predator_species | 0 | 1.0000000 | 12 | 20 | 0 | 13 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | Predator_development_stage | 65 | 0.8911223 | 5 | 8 | 0 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | Predator_sex | 319 | 0.4656616 | 4 | 6 | 0 | 2 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | Study_site | 0 | 1.0000000 | 5 | 5 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | Collection_date | 0 | 1.0000000 | 8 | 10 | 0 | 8 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | Predator_individual_number | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3155.5946399 | 690.5657635 | 1895.0000000 | 2613.000000 | 3167.000000 | 3819.00000 | 4246.00000 | ▅▇▆▅▇ |
| numeric | Predator_body_length_[mm_measured] | 1 | 0.9983250 | NA | NA | NA | NA | NA | 9.2290268 | 5.0715863 | 2.0000000 | 6.000000 | 8.000000 | 11.00000 | 47.00000 | ▇▂▁▁▁ |
| numeric | Predator_body_mass_[mg_calculated]_ | 1 | 0.9983250 | NA | NA | NA | NA | NA | 8.7169073 | 6.7999583 | 0.2888016 | 3.726161 | 7.279457 | 12.23742 | 48.07547 | ▇▃▁▁▁ |
| numeric | Consumption_prey_Araneae | 0 | 1.0000000 | NA | NA | NA | NA | NA | 0.0201005 | 0.1404618 | 0.0000000 | 0.000000 | 0.000000 | 0.00000 | 1.00000 | ▇▁▁▁▁ |
| numeric | Consumption_prey_Diptera | 0 | 1.0000000 | NA | NA | NA | NA | NA | 0.1155779 | 0.3199862 | 0.0000000 | 0.000000 | 0.000000 | 0.00000 | 1.00000 | ▇▁▁▁▁ |
| numeric | Consumption_prey_Gamasidae | 0 | 1.0000000 | NA | NA | NA | NA | NA | 0.0418760 | 0.2004738 | 0.0000000 | 0.000000 | 0.000000 | 0.00000 | 1.00000 | ▇▁▁▁▁ |
| numeric | Consumption_prey_Isopoda | 0 | 1.0000000 | NA | NA | NA | NA | NA | 0.0452261 | 0.2079740 | 0.0000000 | 0.000000 | 0.000000 | 0.00000 | 1.00000 | ▇▁▁▁▁ |
| numeric | Consumption_prey_Lumbricidae | 0 | 1.0000000 | NA | NA | NA | NA | NA | 0.0938023 | 0.2917980 | 0.0000000 | 0.000000 | 0.000000 | 0.00000 | 1.00000 | ▇▁▁▁▁ |
| numeric | Consumption_prey_Oribatida | 0 | 1.0000000 | NA | NA | NA | NA | NA | 0.0134003 | 0.1150780 | 0.0000000 | 0.000000 | 0.000000 | 0.00000 | 1.00000 | ▇▁▁▁▁ |
| numeric | Consumption_prey_Staphylinidae | 0 | 1.0000000 | NA | NA | NA | NA | NA | 0.0301508 | 0.1711454 | 0.0000000 | 0.000000 | 0.000000 | 0.00000 | 1.00000 | ▇▁▁▁▁ |
| numeric | Consumption_prey_Collembola | 0 | 1.0000000 | NA | NA | NA | NA | NA | 0.2847571 | 0.4516771 | 0.0000000 | 0.000000 | 0.000000 | 1.00000 | 1.00000 | ▇▁▁▁▃ |
| numeric | Consumption_prey_Ceratophysella_denticulata | 312 | 0.4773869 | NA | NA | NA | NA | NA | 0.0070175 | 0.0836232 | 0.0000000 | 0.000000 | 0.000000 | 0.00000 | 1.00000 | ▇▁▁▁▁ |
| numeric | Consumption_prey_Folsomia_quadrioculata | 312 | 0.4773869 | NA | NA | NA | NA | NA | 0.0912281 | 0.2884396 | 0.0000000 | 0.000000 | 0.000000 | 0.00000 | 1.00000 | ▇▁▁▁▁ |
| numeric | Consumption_prey_Lepidocyrtus_lanuginosus | 312 | 0.4773869 | NA | NA | NA | NA | NA | 0.0280702 | 0.1654639 | 0.0000000 | 0.000000 | 0.000000 | 0.00000 | 1.00000 | ▇▁▁▁▁ |
| numeric | Consumption_prey_Protaphorura_armata | 312 | 0.4773869 | NA | NA | NA | NA | NA | 0.0315789 | 0.1751839 | 0.0000000 | 0.000000 | 0.000000 | 0.00000 | 1.00000 | ▇▁▁▁▁ |
| numeric | Consumption_prey_Pogonognathellus_longicornis | 312 | 0.4773869 | NA | NA | NA | NA | NA | 0.0070175 | 0.0836232 | 0.0000000 | 0.000000 | 0.000000 | 0.00000 | 1.00000 | ▇▁▁▁▁ |
| numeric | Consumption_prey_Lithobiidae | 532 | 0.1088777 | NA | NA | NA | NA | NA | 0.0000000 | 0.0000000 | 0.0000000 | 0.000000 | 0.000000 | 0.00000 | 0.00000 | ▁▁▇▁▁ |
First, an image
#knitr::include_graphics(path to graphics)List of Predator Species
df$Predator_species %>% unique## [1] "Lithobius_mutabilis" "Lithobius_aulacopus" "Lithobius_sp"
## [4] "Lithobius_crassipes" "Lithobius_nodulipes" "Lithobius_dentatus"
## [7] "Lithobius_melanops" "Lithobius_muticus" "Lithobius_curtipes"
## [10] "Lithobius_piceus" "Geophilus_sp" "Schendyla_nemorensis"
## [13] "Strigamia_acuminata"df$prey_richness <-
df %>% select(starts_with("Consumption")) %>% rowSums(na.rm=TRUE)
p <- df %>%
ggplot(aes(x=Predator_development_stage,y=prey_richness,color=Predator_sex))+
geom_boxplot() +
theme_bw()
plotly::ggplotly(p)